This invention generally concerns nuclear reactor operations optimization and management. More particularly, the present invention is directed toward identifying optimal reactor plant operational settings and an ongoing management strategy that incorporates a consideration of plant-specific constraints for a multiplicity of operational control-variables such as, for example, control blade positioning, cycle flow strategy, location of sequence exchanges, and other critical-to-quality control variables relevant to operation of a nuclear reactor plant throughout one or more reactor core refueling cycles.
A nuclear reactor plant includes many different individual components that have dynamic characteristics which may affect any given strategy devised for eliciting a more efficient operation of the reactor core. For example, a nuclear reactor core has many, e.g., several hundred, control blades which each require position and location identification throughout the direction of one or more cycles of fuel depletion. In addition, many other controllable elements and factors that affect the reactivity and overall efficiency of a reactor core must also be taken into consideration if one is to design or develop an effective control strategy for optimizing the performance of a reactor core at a particular reactor plant. Such variable “operational controls” (also referred to herein as “independent control-variables”) include various physical component and controllable operating conditions configurations within the reactor that can be individually adjusted or set before or during operation of the reactor.
For example, the locations of the various control blades within the reactor core are one of the many independent controllable variables that significantly affect the generated power output and overall efficiency of operation of a reactor. Other operational controls include such controllable variables as “core flow” (rate of water flow through the core) and the timing of the sequence exchange or exposure interval at which groups of control blades are changed. Each of these so called variable operational controls may be considered as an independent “control-variable” which has a measurable effect on the overall performance of the reactor core. Due to vast number of possible different operational values and combinations of values that these independent control-variables can assume, it is both a formidable challenge and a very time consuming task using conventional computer-aided methodologies to attempt to analyze and optimize most if not all of the individual influences that may have an impact core reactivity and performance.
In order to furnish and maintain a required energy output, the reactor core is periodically refueled with fresh fuel bundles. The duration between one refueling and the next is commonly referred to as a “fuel cycle”, “core cycle”, or “cycle” of operations and, depending on the particular reactor plant, is on the order of twelve to twenty-four months. During the course of a cycle, the excess energy capability of the core, defined as the excess reactivity or “hot excess”, is controlled by core coolant (water) flow and the control blades. Typically, a reactor core contains many such control blades which are fit between selected fuel bundles and are axially positioned within the core.
The total number of control blades utilized in a reactor varies with core size and geometry, and is typically between fifty (50) and one-hundred and fifty (150). The axial position of control blades (e.g., fully inserted, fully withdrawn, or somewhere in between) is based on the need to control excess reactivity and to meet other operational constraints, such as thermal or reactivity margins. For each control blade, there may be, for example, twenty-five or more possible axial positions and twenty-five or more “exposure” (duration of use) steps. Considering symmetry and other requirements that may reduce the number of control blades that are available for application at any one time, there are more than several million existing combinations of control blade positions possible for even the simplest arrangement. Larger arrangements may have more than a googol (1×10100) possible configurations. However, only a small fraction of these configurations will satisfy all the applicable design and safety constraints, and of those, only a smaller fraction prove economical. Moreover, the axial positioning of control blades also influences core cycle energy and potential thermal limits. Since it is desirable to maximize the core-cycle energy in order to minimize nuclear fuel cycle costs, developing an optimum control blade positioning strategy is yet another type of independent control-variable optimization problem that should be taken into consideration when attempting to optimize operational management strategies.
Historically, cycle operations and core management, including control blade positioning, sequence exchange lengths, and core flow selection, are determined on a “trial-and-error” basis based primarily on the past experiences of the reactor engineers. Due to circumstances that require a rapid response to changing plant operating conditions, a reactor engineer may be faced with the formidable challenge of specifying values for over one-hundred or more independent control variables within a very short time frame. If a particular design constraint is not satisfied by an identified arrangement, then the arrangement is modified and a computer simulation is run. Because of the relatively long computer simulation time required for assessing the impact of a change in the value of even a single given independent control-variable, man-weeks of human and computer resources are typically required before an appropriate operational strategy is identified using this procedure. Moreover, using this trial-and-error approach, once a operation strategy that satisfies all identifiable design and safety constraints is determined, it may turn out that the identified arrangement does not provide the best cycle-energy economics. In that case, the trial-and-error selection process must continue until the engineers believe that an optimum operational strategy has been identified. In practice, however, it is very possible that a particular core arrangement that is inconsistent with past experience may actually be the optimum operational strategy to use.
Numerous systems have attempted to address various aspects of the above problem through the implementation of various improvements in display interfaces to the reactor engineer (e.g., see U.S. Pat. Nos. 5,859,885, 4,853,175 and 5,812,622), improvements in data management of information (e.g., see U.S. Pat. Nos. 5,793,636 and 4,459,259), improvements towards reactor operation alarms (e.g., see U.S. Pat. Nos. 5,311,562 and 5,023,045), and improvements in the instantaneous monitoring of the reactor (e.g., see U.S. Pat. Nos. 4,997,617, 5,309,485; 5,392,320 and 5,091,139). Although such efforts have somewhat improved the real-time monitoring and display of information required for operation of a nuclear reactor, none provide the tools needed for determining the appropriate settings of the independent control variables for an entire full cycle or longer. Moreover, the above prior art systems all rely significantly on a manual input/data selection process in the development of any operational strategy.
There have been a few attempts to provide automated predictive capabilities for one or more aspects of the above problem through the use of so called “decision tree”, or “neural net” technologies. For example, U.S. Pat. No. 4,552,718 to Impink, Jr. et al. discloses a system for monitoring the operational status of a nuclear reactor that provides indication of “off-normal” conditions, and a path of operation by which the reactor can be restored to normal conditions by way of “decision tree” logic. Such “decision tree” technologies are capable of providing correct logic to a limited number of independent variables when adequate supporting data is provided such that the cause and effect relationships are well defined, such as global reactor problems and consequent human responses. However, optimization of all current and future independent variables as described above for operation of a boiling water reactor (BWR) for entire full cycle of operations essentially requires an infinite number of cause/effect relations and is not particularly feasible. Similarly, U.S. Pat. No. 5,009,833 to Takeuchi et al. describes an expert system rule based optimization approach. Much like “decision tree” technologies, “expert system” rule based technologies are only as good as the rules provided to the system. Consequently, while these technologies are capable of identifying global reactor issues and the subsequent necessary human response, their application is often not practical and does not include an application for a continuous operations optimization of a working nuclear reactor.
In another example, U.S. Pat. No. 5,790,616 to R. O. Jackson et al., issued Aug. 4, 1998, describes an early attempt to perform optimization on control blade locations for a nuclear reactor. In this example, optimizations are performed using a genetics based algorithm at a single time sequence. Once the preferred rod patterns at a given time sequence are determined, the rods are established and the following time step is studied. A heuristic assumption is integrated into the system by assuming that the “best” set of rod patterns for the cycle are the rod patterns that provide for the lowest axial peak in the core. Although this heuristic assumption enabled the Jackson et al. system to optimize on a subset of the total number of independent variables (approximately 6-12), the assumption precludes the obtainment of a true optimal solution. Moreover, on many BWR reactors, extremely hard bottom burns at the beginning of cycle (BOC) can lead to conditions at the end of cycle (BOC)—where thermal limits are excessive and require the reactor to lower power levels to maintain safe operation. Consequently, the system disclosed by Jackson et al. provides neither optimal nor potentially usable solutions.
To more adequately address the above problems, it would be most desirable to have a very efficient computer system arrangement that would be capable of performing a comprehensive nuclear reactor plant operations optimization process that would identify most, if not all, of the appropriate changes/modifications in operational control-variables that are needed to result in an improved fuel cycle efficiency, better global reactor economics and enhanced operational flexibility.